Steps

0. Install and load libraries

1. Read in the data

There are 4 datasets to read in, Starbucks locations, Starbucks nutrition, US population by state, and US state abbreviations. All of them are on the course GitHub.

sb_locs <- read_csv("https://raw.githubusercontent.com/JSC370/JSC370-2025/refs/heads/main/data/starbucks/starbucks-locations.csv")

## Rows: 25600 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (10): Store Number, Store Name, Ownership Type, Street Address, City, St...
## dbl  (2): Longitude, Latitude
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

sb_nutr <- read_csv("https://raw.githubusercontent.com/JSC370/JSC370-2025/refs/heads/main/data/starbucks/starbucks-menu-nutrition.csv")

## Rows: 205 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Item, Category
## dbl (5): Calories, Fat (g), Carb. (g), Fiber (g), Protein (g)
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

usa_pop <- read_csv("https://raw.githubusercontent.com/JSC370/JSC370-2025/refs/heads/main/data/starbucks/us_state_pop.csv")

## Rows: 55 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (1): population
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

usa_states<-read_csv("https://raw.githubusercontent.com/JSC370/JSC370-2025/refs/heads/main/data/starbucks/states.csv")

## Rows: 51 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): State, Abbreviation
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

2. Look at the data

Inspect each dataset to look at variable names and ensure it was imported correctly.

# Starbuck location
head(sb_locs)

## # A tibble: 6 × 12
##   `Store Number` `Store Name`            `Ownership Type` `Street Address` City 
##   <chr>          <chr>                   <chr>            <chr>            <chr>
## 1 47370-257954   Meritxell, 96           Licensed         Av. Meritxell, … Ando…
## 2 22331-212325   Ajman Drive Thru        Licensed         1 Street 69, Al… Ajman
## 3 47089-256771   Dana Mall               Licensed         Sheikh Khalifa … Ajman
## 4 22126-218024   Twofour 54              Licensed         Al Salam Street  Abu …
## 5 17127-178586   Al Ain Tower            Licensed         Khaldiya Area, … Abu …
## 6 17688-182164   Dalma Mall, Ground Flo… Licensed         Dalma Mall, Mus… Abu …
## # ℹ 7 more variables: `State/Province` <chr>, Country <chr>, Postcode <chr>,
## #   `Phone Number` <chr>, Timezone <chr>, Longitude <dbl>, Latitude <dbl>

head(sb_nutr)

## # A tibble: 6 × 7
##   Item         Category Calories `Fat (g)` `Carb. (g)` `Fiber (g)` `Protein (g)`
##   <chr>        <chr>       <dbl>     <dbl>       <dbl>       <dbl>         <dbl>
## 1 Chonga Bagel Food          300         5          50           3            12
## 2 8-Grain Roll Food          380         6          70           7            10
## 3 Almond Croi… Food          410        22          45           3            10
## 4 Apple Fritt… Food          460        23          56           2             7
## 5 Banana Nut … Food          420        22          52           2             6
## 6 Blueberry M… Food          380        16          53           1             6

head(usa_pop)

## # A tibble: 6 × 2
##   state      population
##   <chr>           <dbl>
## 1 Alabama       4779736
## 2 Alaska         710231
## 3 Arizona       6392017
## 4 Arkansas      2915918
## 5 California   37253956
## 6 Colorado      5029196

head(usa_states)

## # A tibble: 6 × 2
##   State      Abbreviation
##   <chr>      <chr>       
## 1 Alabama    AL          
## 2 Alaska     AK          
## 3 Arizona    AZ          
## 4 Arkansas   AR          
## 5 California CA          
## 6 Colorado   CO

3. Format and merge the data

Subset Starbucks data to the US.
Create counts of Starbucks stores by state.
Merge population in with the store count by state.
Inspect the range values for each variable.

sb_usa <- sb_locs |> filter(Country == "US")

sb_locs_state <- sb_usa |>
  rename(state = 'State/Province') |>
  group_by(state) |>
  summarize(n_stores = n())

# need state abbreviations
usa_pop_abbr <- 
  full_join(usa_pop, usa_states,
            by = join_by(state == State) 
            ) 
  
sb_locs_state <- full_join(sb_locs_state, usa_pop_abbr,
                           by = join_by(state == Abbreviation)
                           )

4. Use `ggplotly` for EDA

Answer the following questions:

Are the number of Starbucks proportional to the population of a state? (scatterplot)
Is the caloric distribution of Starbucks menu items different for drinks and food? (histogram)
What are the top 20 words in Starbucks menu items? (bar plot)

p1 <- ggplot(sb_locs_state, aes(x = population, y = n_stores, color = state)) + 
  geom_point(alpha = 0.8) + 
  theme_bw()

ggplotly(p1)

4a) Answer: The scatterplot suggests that the number of Starbucks stores in a state is roughly proportional to the population, as we observe a positive correlation between the number of stores (n_stores) and population (population). However, there is some variation, with certain states having more or fewer stores than expected based on population alone. California (CA) appears to have the highest number of stores, aligning with its large population.

p2 <- ggplot(sb_nutr, aes(x=Calories, fill=Category)) + 
  geom_histogram(alpha= 0.5) + 
  theme_bw()

ggplotly(p2)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

4b) Answer: The caloric distribution of Starbucks menu items differs between drinks and food. Drinks tend to have lower calories, mostly below 300, while food items are more evenly distributed and extend beyond 600 calories. There is some overlap in the mid-range (200-400 calories), but food generally has higher caloric content

p3 <- sb_nutr |>
  unnest_tokens(word, Item, token = "words") |>
  count(word, sort = T) |>
  head(20) |>
  ggplot(aes(fct_reorder(word, n), n)) +
  geom_col() +
  coord_flip() +
  theme_bw()

ggplotly(p3)

4c) Answer: The top 20 words in Starbucks menu items include a mix of beverages and food-related terms. “Iced,” “tazo,” and “bottled” are the most frequent, indicating a strong presence of iced drinks and bottled products. Common food-related words like “sandwich,” “chocolate,” “egg,” and “protein” suggest a significant portion of the menu consists of meals and snacks. Additionally, coffee-related terms such as “mocha,” “latte,” and “macchiato” highlight the brand’s focus on coffee beverages.

5. Scatterplots using `plot_ly()`

Create a scatterplot using plot_ly() representing the relationship between calories and carbs. Color the points by category (food or beverage). Is there a relationship, and do food or beverages tend to have more calories?

sb_nutr |>
  plot_ly(x = ~Calories, y = ~`Carb. (g)`, type = "scatter", mode = "markers", color  = ~Category)

## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels

5a) Answer: The scatterplot shows a positive correlation between calories and carbohydrates, indicating that items with more calories tend to have higher carbohydrate content. Food items (blue points) generally have higher calorie counts, with many exceeding 400 calories, while drinks (green points) tend to have fewer calories, mostly below 300. However, drinks still contain a significant amount of carbohydrates, suggesting that some beverages are high in sugar. Overall, food items tend to have more calories than beverages, but both categories follow a similar calorie-to-carb trend.
Repeat this scatterplot but for the items that include the top 10 words. Color again by category, and add hoverinfo specifying the word in the item name. Add layout information to title the chart and the axes, and enable hovermode = "compare".
What are the top 10 words and is the plot much different than above?

topwords <- sb_nutr |>
  unnest_tokens(word, Item, token = "words") |>
  group_by(word) |>
  summarise(word_frequency = n()) |>
  arrange(across(word_frequency, desc)) |>
  head(10)

sb_nutr |>
  unnest_tokens(word, Item, token = "words") |>
  filter(word %in% topwords$word) |>
  plot_ly(
    x = ~Calories, 
    y = ~`Carb. (g)`, 
    type = "scatter", 
    mode = "markers", 
    color  = ~Category, 
    hoverinfo = "text", 
    text = ~paste0("Item: ", word)) |>
  layout(
    title = "Calories vs Carbohydrate",
    xaxis = list(title = "Calories"),
    yaxis = list(title = "Carb. (g)"),
    hovermode = "compare"
         )

## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels

5b) Answer: The top 10 words in Starbucks menu items are: iced, bottled, tazo, sandwich, chocolate, coffee, egg, Starbucks, tea, and black. The plot is pretty much the same as the original, showing a similar positive correlation between calories and carbohydrates.

6. `plot_ly` Boxplots

Create a boxplot of all of the nutritional variables in groups by the 10 item words.
Which top word has the most calories? Which top word has the most protein?

sb_nutr_long <- sb_nutr |>
  unnest_tokens(word, Item, token="words") |>
  filter(word %in% topwords$word) |>
  pivot_longer(
    cols = c(Calories, `Fat (g)`, `Carb. (g)`, `Fiber (g)`, `Protein (g)`),
    names_to = "Nutrient", values_to = "Value")

plot_ly(data = sb_nutr_long,
        x = ~word,
        y = ~Value,
        color = ~Nutrient,
        type = 'box'
  ) |>
  layout(
    title = "Nutrition values for the top 10 words items",
    xaxis = list(title = 'Item Word'),
    yaxis = list(title = 'Nutrition Value'),
    hovermode = 'compare'
  )

1. Answer: The word with the most calories appears to be “sandwich”, as it has the highest median and range in the calorie boxplot. The word with the most protein is likely “egg”, as its protein values are consistently higher than the other categories.

7. 3D Scatterplot

Create a 3D scatterplot between Calories, Carbs, and Protein for the items containing the top 10 words
Do you see any patterns (clusters or trends)?

sb_nutr |> unnest_tokens(word, Item, token="words") |>
  filter(word %in% topwords$word[1:10]) |> 
  plot_ly(
    x = ~Calories,
    y = ~`Carb. (g)`,
    z = ~`Protein (g)`,
    color = ~word,
    type = "scatter3d",
    mode = "markers",
    marker = list(size = 5)
  ) |>
  layout(
    title = "3D Scatterplot of Calories, Carbs, and Protein",
    scene = list(
      xaxis = list(title = "Calories"),
      yaxis = list(title = "Carbohydrates (g)"),
      zaxis = list(title = "Protein (g)")
    )
  )

## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors

1. Answer: The 3D scatterplot shows a positive correlation between calories, carbohydrates, and protein. Food items like sandwich and egg tend to have higher calories and protein, while drinks like iced and tea cluster at lower calories but still contain carbohydrates. This suggests a clear distinction between high-protein, high-calorie food items and carb-heavy beverages.

8. `plot_ly` Map

Create a map to visualize the number of stores per state, and another for the population by state. Add custom hover text. Use subplot to put the maps side by side.
Describe the differences if any.

# Set up mapping details
set_map_details <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('steelblue')
)

# Make sure both maps are on the same color scale
shadeLimit <- 125

# Create hover text
sb_locs_state$hover <- with(sb_locs_state, paste("Number of Starbucks: ", n_stores, '<br>', "State: ", state.y, '<br>', "Population: ", population))

# Create the map
map1 <- plot_geo(sb_locs_state, locationmode = "USA-states") |>
  add_trace(z = ~n_stores, text = ~hover, locations = ~state,
            color = ~n_stores, colors = 'Purples') |>
  layout(title = "Starbucks store by state", geo = set_map_details)
# map1


map2 <- plot_geo(sb_locs_state, locationmode = "USA-states") |>
  add_trace(z = ~population, text = ~hover, locations = ~state,
            color = ~population, colors = 'Purples') |>
  layout(title = "Starbucks store by state", geo = set_map_details)
# map2

subplot(map1, map2)

## Warning: Ignoring 4 observations

1. Answer: The two maps show Starbucks store distribution (left) and population distribution (right) by state. While there is a general correlation between population and the number of Starbucks locations, California stands out as having the most stores and the highest population. However, some states with high populations, such as Texas and Florida, have fewer stores relative to their population size compared to California.

Lab 11 - Interactive Visualization

Learning Goals

Lab Description

Deliverables

Steps

0. Install and load libraries

1. Read in the data

2. Look at the data

3. Format and merge the data

4. Use `ggplotly` for EDA

5. Scatterplots using `plot_ly()`

6. `plot_ly` Boxplots

7. 3D Scatterplot

8. `plot_ly` Map

Lab 11 - Interactive Visualization

Learning Goals

Lab Description

Deliverables

Steps

0. Install and load libraries

1. Read in the data

2. Look at the data

3. Format and merge the data

4. Use ggplotly for EDA

5. Scatterplots using plot_ly()

6. plot_ly Boxplots

7. 3D Scatterplot

8. plot_ly Map

4. Use `ggplotly` for EDA

5. Scatterplots using `plot_ly()`

6. `plot_ly` Boxplots

8. `plot_ly` Map